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by Allen B. Downey
The main features of allendowney/thinkbayes2 are: Mathematics and Statistics.
Projects with overlapping indexed features include: alexeygrigorev/data-science-interviews — This project is a curated knowledge repository providing theoretical guides, practical challenge banks, and… allendowney/thinkstats — Notebooks for the third edition of Think Stats. amitkaps/hackermath — ― Richard Feynman. camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers — This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for… empathy87/the-elements-of-statistical-learning-python-notebooks — Reproducing examples from the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome… fastai/numerical-linear-algebra — This project is a comprehensive library for numerical linear algebra and scientific computing, designed to provide…
This project is a curated knowledge repository providing theoretical guides, practical challenge banks, and professional handbooks for technical interview preparation in data science and machine learning. It serves as a comprehensive study resource that combines theoretical knowledge with algorithmic practice. The repository features specialized study resources including a probability and statistics handbook, a machine learning reference for algorithms and neural network architectures, and a coding and SQL challenge bank designed to simulate recruitment assignments. It also includes a technic
This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for performing statistical inference and quantifying uncertainty through a probabilistic programming workflow using Python. The resource employs a computation-first pedagogy, teaching Bayesian methods and parameter estimation through executable code and simulations instead of formal mathematical notation. It provides a practical approach to implementing Markov Chain Monte Carlo sampling to estimate posterior distributions. The content covers building probabilistic models, integrating e
Notebooks for the third edition of Think Stats
